Statistical Analysis of Phenotyping Data

The project involved the statistical analysis of maize data from various phenotyping platforms. The mission took place between September 2019 and August 2020, in collaboration with the LEPSE laboratory (Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux). The development was supervised by the Information Systems Department (DSI) of INRAE (National Research Institute for Agriculture, Food and Environment), with the UMR (Joint Research Unit) LEPSE responsible for scientific and business aspects.

Tasks & Objectives

As a data scientist, my role involved cleaning and analyzing data from various phenotyping platforms, while working on the development of statistical models to understand the interaction between genotype and environment. One of the main objectives was to ensure the quality and comparability of data from different sources.

Success criteria included not only the development of robust statistical models but also the production of clear and informative visualizations to illustrate the differences between maize genotypes under varying environmental conditions. A key objective was to establish a rigorous standardization of measurement protocols to ensure the comparability of results. Finally, it was essential to develop a comprehensive documentation of the analysis process.

Actions and Development

My first step was to familiarize myself with the data from various phenotyping platforms, including understanding the measurement protocols and data formats. I then developed a series of R scripts to clean and standardize the data, followed by statistical analysis using various packages. For visualization, I used ggplot2 to create informative plots.

Regular exchanges with the project, scientific, and IT teams, as well as with the former development team, facilitated my work. Collaboration with the LEPSE UMR was crucial for developing a common understanding of the data and establishing a shared vocabulary. Despite the complexity of the data and significant variations in measurement protocols, implementing a standardized analysis process represented a major challenge but also a learning opportunity.

Key decisions were made collectively during bi-weekly meetings. For the statistical analysis, I presented a Proof of Concept (POC) before implementing the complete solution.

Results

The results are multiple: development of robust statistical models, production of clear and informative visualizations, and establishment of a rigorous standardization of measurement protocols. The analysis process allowed for a better understanding of the interaction between genotype and environment, while the visualizations provided valuable insights for researchers. Additionally, the documentation of the analysis process provided a valuable resource for future studies.

I learned to effectively handle complex and heterogeneous data, to develop robust statistical models, and to create informative visualizations. Finally, the experience of working with phenotyping data strengthened my understanding of the challenges in agricultural research and improved my ability to communicate complex ideas clearly.

Technical Stack

The technologies used include: R, ggplot2, data.table, as well as Markdown for documentation. For the statistical analysis, I chose to use a combination of R packages, while other technological choices were made to align with the project objectives. The analysis, complex in terms of both data handling and statistical modeling, required mastery of both data science and domain-specific knowledge. Existing data quality issues also posed a challenge, which I addressed by developing robust data cleaning and standardization procedures. Finally, learning to effectively use R for statistical analysis constituted an important step in improving the analysis process.